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Universal Analytical Forms for Modeling Image Probabilities
September 2002 (vol. 24 no. 9)
pp. 1200-1214

Abstract—Seeking probability models for images, we employ a spectral approach where the images are decomposed using bandpass filters and probability models are imposed on the filter outputs (also called spectral components). We employ a (two-parameter) family of probability densities, introduced in [11] and called Bessel K forms, for modeling the marginal densities of the spectral components, and demonstrate their fit to the observed histograms for video, infrared, and range images. Motivated by object-based models for image analysis, a relationship between the Bessel parameters and the imaged objects is established. Using \big. L^2\hbox{-}{\rm{metric}}\bigr. on the set of Bessel K forms, we propose a pseudometric on the image space for quantifying image similarities/differences. Some applications, including clutter classification and pruning of hypotheses for target recognition, are presented.

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Index Terms:
Image probabilities, spectral analysis, Bessel K forms, clutter classification, target recognition, Gabor filters.
Citation:
Anuj Srivastava, Xiuwen Liu, Ulf Grenander, "Universal Analytical Forms for Modeling Image Probabilities," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 9, pp. 1200-1214, Sept. 2002, doi:10.1109/TPAMI.2002.1033212
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